Segmentation of Melanoma Skin Lesion Using Perceptual Color Difference Saliency with Morphological Analysis

The prevalence of melanoma skin cancer disease is rapidly increasing as recorded death cases of its patients continue to annually escalate. Reliable segmentation of skin lesion is one essential requirement of an efficient noninvasive computer aided diagnosis tool for accelerating the identification process of melanoma. This paper presents a new algorithm based on perceptual color difference saliency along with binary morphological analysis for segmentation of melanoma skin lesion in dermoscopic images. The new algorithm is compared with existing image segmentation algorithms on benchmark dermoscopic images acquired from public corpora. Results of both qualitative and quantitative evaluations of the new algorithm are encouraging as the algorithm performs excellently in comparison with the existing image segmentation algorithms.

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Directional Vector-Based Skin Lesion Segmentation — A Novel Approach to Skin Segmentation

International Journal of Image and Graphics ◽

10.1142/s0219467820500217 ◽

2020 ◽

Vol 20 (03) ◽

pp. 2050021

Author(s):

P. Nikesh ◽

G. Raju

Keyword(s):

Skin Lesion ◽

Automatic Segmentation ◽

High Accuracy ◽

Lesion Segmentation ◽

Seed Point ◽

Skin Segmentation ◽

Novel Approach ◽

Segmentation Algorithms ◽

Computer Aided ◽

Aided Diagnosis

Efficient skin lesion segmentation algorithms are required for computer aided diagnosis of skin cancer. Several algorithms were proposed for skin lesion segmentation. The existing algorithms are short of achieving ideal performance. In this paper, a novel semi-automatic segmentation algorithm is proposed. The fare concept of the proposed is 8-directional search based on threshold for lesion pixel, starting from a user provided seed point. The proposed approach is tested on 200 images from PH2 and 900 images from ISBI 2016 datasets. In comparison to a chosen set of algorithms, the proposed approach gives high accuracy and specificity values. A significant advantage of the proposed method is the ability to deal with discontinuities in the lesion.

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Detection of Melanoma Skin Cancer using Convolutional Neural Network algorithm

International Journal of Innovative Technology and Exploring Engineering - Special Issue ◽

10.35940/ijitee.f4636.059720 ◽

2020 ◽

Vol 9 (7) ◽

pp. 115-118

Keyword(s):

Neural Network ◽

Image Segmentation ◽

Skin Cancer ◽

Skin Lesion ◽

Convolutional Neural Network ◽

Data Augmentation ◽

Health Sector ◽

Network Algorithm ◽

Neural Network Algorithm ◽

Melanoma Skin

Skin Cancer, a health issue which might cause severe consequences if not detected and controlled properly. Since there is a huge evolution in the health sector because of development in computer technologies, it is possible to analyze images efficiently and make correct decisions. Deep learning algorithms can be used for analyzing dermoscopic images by learning features of images in an incremental manner. Aim of our proposed method is to categorize skin lesion image as Benign or Melanoma and also to study the performance of Convolutional Neural Network algorithm using data augmentation technique and without data augmentation technique. The proposed method uses dataset from ISIC archive 2019. Steps involved in the proposed method are Image Pre-Processing, Image Segmentation and Image Classification. Initially, Image Pre-Processing algorithm is performed on skin lesion image. Image Segmentation algorithm is used to obtain Region of Interest (ROI) from pre-processed image. Then, Convolutional Neural Network algorithm classifies image as melanoma or benign. The Proposed method can rapidly detect melanoma skin cancer which aids in starting the treatment process without delay.

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A NOVEL IMAGE SEGMENTATION APPROACH BASED ON NEUTROSOPHIC SET AND IMPROVED FUZZY C-MEANS ALGORITHM

New Mathematics and Natural Computation ◽

10.1142/s1793005711001858 ◽

2011 ◽

Vol 07 (01) ◽

pp. 155-171 ◽

Cited By ~ 37

Author(s):

H. D. CHENG ◽

YANHUI GUO ◽

YINGTAO ZHANG

Keyword(s):

Image Segmentation ◽

Point Of View ◽

Neutrosophic Set ◽

Convergence Criterion ◽

Complex Objects ◽

Fuzzy C Means ◽

Segmentation Methods ◽

Segmentation Algorithms ◽

Segmentation Approach ◽

Fuzzy C Means Algorithm

Image segmentation is an important component in image processing, pattern recognition and computer vision. Many segmentation algorithms have been proposed. However, segmentation methods for both noisy and noise-free images have not been studied in much detail. Neutrosophic set (NS), a part of neutrosophy theory, studies the origin, nature, and scope of neutralities, as well as their interaction with different ideational spectra. However, neutrosophic set needs to be specified and clarified from a technical point of view for a given application or field to demonstrate its usefulness. In this paper, we apply neutrosophic set and define some operations. Neutrosphic set is integrated with an improved fuzzy c-means method and employed for image segmentation. A new operation, α-mean operation, is proposed to reduce the set indeterminacy. An improved fuzzy c-means (IFCM) is proposed based on neutrosophic set. The computation of membership and the convergence criterion of clustering are redefined accordingly. We have conducted experiments on a variety of images. The experimental results demonstrate that the proposed approach can segment images accurately and effectively. Especially, it can segment the clean images and the images having different gray levels and complex objects, which is the most difficult task for image segmentation.

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Comparison of Thresholding and Edge Detection Segmentation Techniques

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.860-863.2783 ◽

2013 ◽

Vol 860-863 ◽

pp. 2783-2786

Author(s):

Yu Bing Dong ◽

Hai Yan Wang ◽

Ming Jing Li

Keyword(s):

Image Segmentation ◽

Root Mean Square Error ◽

Edge Detection ◽

Mean Square Error ◽

Root Mean Square ◽

Experimental Results ◽

Mean Square ◽

Segmentation Method ◽

Segmentation Algorithms

Edge detection and thresholding segmentation algorithms are presented and tested with variety of grayscale images in different fields. In order to analyze and evaluate the quality of image segmentation, Root Mean Square Error is used. The smaller error value is, the better image segmentation effect is. The experimental results show that a segmentation method is not suitable for all images segmentation.

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Diagnosis of Skin Melanoma Cancer using Image Based Computer-Aided Diagnosis System from Dermoscopic Images

International Journal of Research in Advent Technology ◽

10.32622/ijrat.97202107 ◽

2021 ◽

Vol 9 (7) ◽

pp. 1-5

Author(s):

Jidnyasa Zambare ◽

Priya Patil ◽

Neha Chavan

Keyword(s):

Skin Test ◽

Skin Disease ◽

Air Bubbles ◽

Skin Melanoma ◽

Malignant Growth ◽

Skin Injury ◽

Melanoma Cancer ◽

Computer Aided ◽

Aided Diagnosis ◽

Melanoma Skin

Skin malignant growth is the most widely recognized, everything being equal. Between 40 to 50 percent of all disease cases analyzed each year are skin malignant growth. Melanomas represent just four percent of all skin malignant growth cases yet are undeniably more perilous. Of all skin disease-related passing’s, 79 percent are from melanoma. Skin disease can be relieved if distinguished early. To appropriately distinguish melanoma, there is a need for a skin test. This is an obtrusive method and is the reason there is a requirement of a conclusion framework that can annihilate the skin test strategy emerges. We proposed to build up a Computer-Aided System that is equipped for ordering a skin injury as threatening or favorable by utilizing the ABCD rule which represents Asymmetry, Border, Color, Diameter of the skin sore. Further, the preprocessed pictures are portioned and commotions are taken out from the Dermoscopic pictures for instance hair and air bubbles. Also, finally, by utilizing a classifier, the proposed system identifies the pictures as favorable or harmful.

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Image Segmentation Techniques in Bone Structure Psychiatry

International Journal Of Engineering And Computer Science ◽

10.18535/ijecs/v9i07.4502 ◽

2020 ◽

Vol 9 (07) ◽

pp. 25102-25112

Author(s):

Ajayi Olayinka Adedoyin ◽

Olamide Timothy Tawose ◽

Olu Sunday Adetolaju

Keyword(s):

Image Segmentation ◽

Bone Structure ◽

Performance Comparison ◽

Imaging Systems ◽

Intrinsic Properties ◽

X Ray ◽

Practical Applications ◽

X Ray Imaging ◽

Segmentation Algorithms ◽

Bone Structures

Today, a large number of x-ray images are interpreted in hospitals and computer-aided system that can perform some intelligent task and analysis is needed in order to raise the accuracy and bring down the miss rate in hospitals, particularly when it comes to diagnosis of hairline fractures and fissures in bone joints. This research considered some segmentation techniques that have been used in the processing and analysis of medical images and a system design was proposed to efficiently compare these techniques. The designed system was tested successfully on a hand X-ray image which led to the proposal of simple techniques to eliminate intrinsic properties of x-ray imaging systems such as noise. The performance and accuracy of image segmentation techniques in bone structures were compared and these eliminated time wasting on the choice of image segmentation algorithms. Although there are several practical applications of image segmentation such as content-based image retrieval, machine vision, medical imaging, object detection, recognition tasks, etc., this study focuses on the performance comparison of several image segmentation techniques for medical X-ray images.

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